Conventional single camera systems are hindered by limited abilities to accurately detect vehicle speed due to limitations associated with viewing a 3D world with 2D imaging devices. Additionally, the quality of evidentiary photos provided by such systems is unsatisfactory due to the retro-reflective properties of license plates, which requires a sensor operating at high dynamic range at night. Moreover, the camera field of view (FOV) conventionally is calibrated for speed detection accuracy, which conflicts with larger FOV requirements in traffic monitoring and incident detection. The performance of systems with such wide FOV in speed estimation tasks typically exhibits a large degree of estimation error unless additional elements and/or features are included, such as multi-view capabilities, structured illumination, stereo-vision, etc. These FOV problems cannot be easily solved with a conventional speed camera. Additionally, classical video-based speed estimate systems based on a single camera exhibit performance and utility that falls short in several areas. For instance, using such systems, the estimated speed is not accurate due to ambiguities introduced by mapping a 3D scene onto a 2D image.
There is a need in the art for systems and methods that facilitate video-based speed estimation and vehicle speed limit enforcement with reduced cost and improved accuracy, while overcoming the aforementioned deficiencies.